SporeAgent: Reinforced Scene-Level Plausibility for Object Pose Refinement

Dominik Bauer, Timothy Patten, Markus Vincze; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022, pp. 654-662

Abstract


Observational noise, inaccurate segmentation and ambiguity due to symmetry and occlusion lead to inaccurate object pose estimates. While depth- and RGB-based pose refinement approaches increase the accuracy of the resulting pose estimates, they are susceptible to ambiguity in the observation as they consider visual alignment. We propose to leverage the fact that we often observe static, rigid scenes. Thus, the objects therein need to be under physically plausible poses. We show that considering plausibility reduces ambiguity and, in consequence, allows poses to be more accurately predicted in cluttered environments. To this end, we extend a recent RL-based registration approach towards iterative refinement of object poses. Experiments on the LINEMOD and YCB-VIDEO datasets demonstrate the state-of-the-art performance of our depth-based refinement approach.

Related Material


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[bibtex]
@InProceedings{Bauer_2022_WACV, author = {Bauer, Dominik and Patten, Timothy and Vincze, Markus}, title = {SporeAgent: Reinforced Scene-Level Plausibility for Object Pose Refinement}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2022}, pages = {654-662} }